Oracle Blog

Thought Leadership on Social Technology and Social Marketing

Tuesday Jun 25, 2013

The following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo.

Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application.

What is the next step?

Augmenting Social Data with other Public Data for More Advanced Analytics

When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced:

It can vary across structured, semi-structured, and unstructured data

It can span across content, profile, and communities of profiles data

It is increasingly public, curated and user generated

The key is not just getting the data, but making it value-added data and using it to help discover the insights to connect to and improve your KPIs.

As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications.

The following diagram shows the requirements for this next generation data and insights service or (DaaS):

Some quick points on these requirements:

Public Data, which in this context is about Common Business Entities, such as -

Customers, Suppliers, Partners, Competitors (all are organizations)

Contacts, Consumers, Employees (all are people)

Products, Brands

This data can be broadly categorized incrementally as -

Base Utility data (address, industry classification)

Public Master Reference data (trade style, hierarchy)

Social/Web data (News, Feeds, Graph)

Transactional Data generated by enterprise process, workflows etc.

This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes -

Change management of Public Reference Data across all categories

Applied Big Data to extract statics as well as real-time insights

Knowledge Diagnostics and Data Mining

As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is:

Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply

Cost-effective: Pre-integrated into applications so customers don’t have to

Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data

Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place

Data-as-a-Service (DaaS)

Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS).

Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration.

Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur.

How do you decide the best approach?

Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value.

The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.).

Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved.

Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined capabilities to extract and integrate the right data from the right sources with the right factoring at the right time for faster decision-making and action within your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage.

Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications.

In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM.

In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc.

This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with.

DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.